Ai-based root cause analysis for telecommunications systems

Abstract

Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.

What is claimed is: 
     
         1 . A method of identifying conditions in a virtualized computing environment providing a telecommunications network running a plurality of network functions, the method comprising:
 receiving, by a computing system, data collected from the telecommunications network, wherein the data comprises:
 time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and 
 raw error logs for the telecommunications systems, the raw error logs associated with the time series telemetry data; 
   using a data parser to identify a type of the data and parsing the data into a standardized format;   inputting the parsed data to a time series insight generator configured to perform quantitative analysis on the parsed data;   inputting the parsed data to a sentiment analyzer configured to perform qualitative analysis on the parsed data; and   combining outputs from the time series insight generator and the sentiment analyzer to generate an output report indicative of anomalous metrics in the telecommunications network; wherein the output report is usable to identify a condition in the telecommunications network, root causes of the identified condition, and recommended actions in response to the identified condition; wherein the output report is usable to initiate an action in the telecommunications network to resolve the identified condition.   
     
     
         2 . The method of  claim 1 , wherein the sentiment analyzer comprises a pre-trained sentiment analysis model. 
     
     
         3 . The method of  claim 1 , wherein the sentiment analyzer is configured to generate numerical scores indicative of a negative sentiment indicative of an anomaly. 
     
     
         4 . The method of  claim 1 , further comprising using a string similarity algorithm to identify duplicate detected negative results. 
     
     
         5 . The method of  claim 1 , further comprising matching qualitative anomalies identified by the sentiment analyzer with quantitative anomalies and insights identified by the time series insight generator. 
     
     
         6 . The method of  claim 1 , further comprising using an anomalous spike/dip detection model to identify spikes and dips in the data. 
     
     
         7 . The method of  claim 1 , further comprising using a failure metric thresholding model to scale metrics by computing a percentage of failure, wherein a metric that exceeds a specified percentage threshold is identified as anomalous. 
     
     
         8 . The method of  claim 1 , further comprising using a trend analysis model to identify upward or downward anomalous trends in the data. 
     
     
         9 . A computing system, comprising:
 one or more processors; and   a computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising:   receiving data collected from a telecommunications network running a plurality of network functions, wherein the data comprises:
 time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and 
 raw error logs collected in conjunction with the time series telemetry data for the telecommunications systems; 
   using a data parser to identify a type of the data parsing the data into a standardized format;   inputting the parsed data to a time series insight generator configured to perform quantitative analysis on the parsed data;   inputting the parsed data to a sentiment analyzer configured to perform qualitative analysis on the parsed data; and   combining outputs from the time series insight generator and the sentiment analyzer to generate an output report indicative of anomalous metrics in the telecommunications network; wherein the output report is usable to identify a condition in the telecommunications network, root causes of the identified condition, and recommended actions in response to the identified condition.   
     
     
         10 . The computing system of  claim 9 , wherein the sentiment analyzer uses a pre-trained sentiment analysis model. 
     
     
         11 . The computing system of  claim 9 , wherein the sentiment analyzer is configured to generate numerical scores indicative of a negative sentiment indicative of an anomaly. 
     
     
         12 . The computing system of  claim 9 , further comprising computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising using a string similarity algorithm to identify duplicate detected negative results. 
     
     
         13 . The computing system of  claim 9 , further comprising computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising matching qualitative anomalies identified by the sentiment analyzer with quantitative anomalies and insights identified by the time series insight generator. 
     
     
         14 . The computing system of  claim 9 , further comprising computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising using an anomalous spike/dip detection model to identify spikes and dips in the data. 
     
     
         15 . The computing system of  claim 9 , further comprising computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising using a failure metric thresholding model to scale metrics by computing a percentage of failure, wherein a metric that exceeds a specified percentage threshold is identified as anomalous. 
     
     
         16 . The computing system of  claim 9 , further comprising computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising using a trend analysis model to identify upward or downward anomalous trends in the data. 
     
     
         17 . A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations comprising:
 receiving data collected from a telecommunications network running a plurality of network functions, wherein the data comprises:
 time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and 
 raw error logs collected with the time series telemetry data for the telecommunications systems; 
   using a data parser to identify a type of the data parsing the data into a standardized format;   inputting the parsed data to a time series insight generator configured to perform quantitative analysis on the parsed data;   inputting the parsed data to a sentiment analyzer configured to perform qualitative analysis on the parsed data; and   combining outputs from the time series insight generator and the sentiment analyzer to generate an output report indicative of anomalous metrics in the telecommunications network; wherein the output report is usable to identify a condition in the telecommunications network and root causes and recommended actions in response to the identified condition.   
     
     
         18 . The computer-readable storage medium of  claim 17 , further comprising computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations comprising using a string similarity algorithm to identify duplicate detected negative results. 
     
     
         19 . The computer-readable storage medium of  claim 18 , further comprising computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations comprising matching qualitative anomalies identified by the sentiment analyzer with quantitative anomalies and insights identified by the time series insight generator. 
     
     
         20 . The computer-readable storage medium of  claim 19 , further comprising computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations comprising using an anomalous spike/dip detection model to identify spikes and dips in the data.